This paper proposes a load monitoring algorithm that exploits the observed time frequency characteristics of a laboratory pulsed load current and uses it to detect events and characterize them into desirable transitions or faults. An electromagnetic gun is assembled at a low voltage lab setup to provide multiple iterations of pulsed load events with a few instances of faults. Detailed analysis of the load profile is followed by a simulation using measured data to demonstrate the effectiveness of the Short Time Fourier Transform based algorithm to identify key events in the current profile and detect faults.


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    Title :

    STFT-Based Event Detection and Classification for a DC Pulsed Load


    Contributors:
    Maqsood, Atif (author) / Rossi, Nick (author) / Ma, Yue (author) / Corzine, Keith (author) / Parsa, Leila (author) / Oslebo, Damian (author)


    Publication date :

    2019-08-01


    Size :

    2897953 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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